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<rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:media="http://search.yahoo.com/mrss/" version="2.0"><channel><title>不系舟记</title><link>https://buxizhou.net</link><atom:link href="https://buxizhou.net/rss.xml" rel="self" type="application/rss+xml"/><description>不系舟记</description><generator>Halo v2.24.2</generator><language>zh-cn</language><lastBuildDate>Wed, 10 Jun 2026 01:15:31 GMT</lastBuildDate><item><title><![CDATA[机器学习中的时间维度与强化学习]]></title><link>https://buxizhou.net/archives/The%20Time%20Dimension%20in%20Machine%20Learning%20and%20Reinforcement%20Learning</link><description><![CDATA[<img src="https://buxizhou.net/plugins/feed/assets/telemetry.gif?title=%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AD%E7%9A%84%E6%97%B6%E9%97%B4%E7%BB%B4%E5%BA%A6%E4%B8%8E%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0&amp;url=/archives/The%20Time%20Dimension%20in%20Machine%20Learning%20and%20Reinforcement%20Learning" width="1" height="1" alt="" style="opacity:0;">强化学习为什么更适合解决动态问题？它不需要预设的标准答案，而是靠智能体在环境里的摸爬滚打，自己找出一套收益最大化的策略。]]></description><guid isPermaLink="false">/archives/The%20Time%20Dimension%20in%20Machine%20Learning%20and%20Reinforcement%20Learning</guid><dc:creator>buxizhou</dc:creator><category>RL</category><pubDate>Fri, 29 May 2026 13:56:29 GMT</pubDate></item></channel></rss>